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Sas statistical software system

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The SAS statistical software system is a suite of applications designed for advanced analytics and data management. It provides a comprehensive platform for statistical analysis, data visualization, and business intelligence. The core function of the SAS statistical software system is to enable users to explore, analyze, and model complex data sets, supporting a wide range of statistical techniques and methodologies.

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SAS software (6040 citations) SAS statistical software (1676 citations) SAS System (230 citations) SAS system software (48 citations) SAS System for Windows statistical software, version 9.4 (3 citations) SAS® statistical software system version 9.3 (3 citations) SAS statistical system software (ver. 9.4) (2 citations)

5 protocols using sas statistical software system

1

Comprehensive Analysis of Clinical Data

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Descriptive and frequency analyses were used to assess clinical and demographic data in all participants, and different subcategories. Categorical data are presented as numbers and percentages. Mean (standard deviation [SD]) values, median (interquartile range [IQR]) values are reported, when appropriate. Data from each domain of the SF-36 were analyzed for the descriptive statistics of mean with standard deviation, median with interquartile range. All statistical analysis was performed using the SAS statistical software system (SAS institute, Inc, Cary, NC).
Al-Rubeaan K., Bana F.A., Alruwaily F.G., Sheshah E., Alnaqeb D., AlQahtani A.M., Ewais D., Al Juhani N., Hassan A.H, & Youssef A.M. (2020). Physicians' choices in the first- and second-line management of type 2 diabetes in the Kingdom of Saudi Arabia. Saudi Pharmaceutical Journal : SPJ, 28(3), 329-337.
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2

EPICOR: Multinational Cardiovascular Study

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For the multinational EPICOR study, the sample size was estimated at 10,600 patients, anticipating that 5 strategies would be employed by ≥10% population and ensuring an 80% power with a 2-sided type I error of 1% (corresponding to a Bonferoni correction for 5 comparisons) to detect a relative risk of at least 1.5 for the comparison of 2 of the groups, assuming a 2-year event rate of 10% for the primary event. Results of the descriptive analysis of the Turkish arm of the study of 1034 patients are presented here.
All statistical analysis was performed by means of the SAS statistical software system (version 9.2, SAS Institute, Inc, Cary, NC).
Ertaş F.S, & Tokgözoğlu L. (2016). Pre- and in-hospital antithrombotic management patterns and in-hospital outcomes in patients with acute coronary syndrome: data from the Turkish arm of the EPICOR study. Anatolian Journal of Cardiology, 16(12), 900-915.
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3

Physical Activity and Metabolic Factors

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For between-group analysis, t-tests or, when data had a skewed distribution, the Mann-Whitney U test was used. Crude and adjusted (not presented) OR:s were calculated for the MetS and its different components, as well as for insulin, IGF-1, IGFBP-1, oestrogen and PA. In the logistic regression models, PA group 3 and 4 (above) were referred to as “high” PA. The “low” PA-group (group 1 and 2) was set as a reference. All statistical analyses were performed using SAS statistical software system version 9.2.
Halldin M., Brismar K., Fahlstadius P., Vikström M., de Faire U, & Hellénius M.L. (2014). The Metabolic Syndrome and ECG Detected Left Ventricular Hypertrophy – Influences from IGF-1 and IGF-Binding Protein-1. PLoS ONE, 9(12), e108872.
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4

Analyzing Nurse Incident Rates and Risk Factors

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Rates from the daily incident forms and hospital records were calculated by dividing the number of incidents by the total number of hours worked by all nurses, then multiplying by 40 so that the rates are presented in units of incidents per nurse per 40-hour work week. Risk factors associated with these rates were analyzed though the use of generalized estimating equations (GEE) with a Poisson model [16 ]. Each hospital was treated as a cluster in this model assuming an autoregressive error structure of lag 1 (AR[1 (link)]). The dependent variable was the weekly number of incidents and an offset of the natural logarithm of hours worked was used so that incident rates were modeled. Independent variables were diagnoses, sex, race/ethnicity, and age groups. Values of independent variables in this model were the percentage of patients during the week who were in each category of each independent variable. The nurse to patient ratio was calculated for each week and each hospital by dividing the total hours all nurses worked in the week by the number of patients for the week. Wald chi-square statistics provided from the GEE models were used to test the association between risk factors and incident rates. All analyses were performed using the SAS statistical software system [17 ].
Ridenour M., Lanza M., Hendricks S., Hartley D., Rierdan J., Zeiss R, & Amandus H. (2015). Incidence and risk factors of workplace violence on psychiatric staff. Work (Reading, Mass.), 51(1), 19-28.
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5

Exploratory Data Analysis Protocol

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Statistical analysis was performed using SAS statistical software system (SAS Institute, Inc, Cary, NC). We used descriptive statistics including frequency tables (n, mean, median, standard deviation, minimum and maximum for continuous variables and n, frequency and percentage for categorical values). The aims of this sub-analysis study did not include statistical hypothesis testing, therefore all data analyses are exploratory and should be interpreted accordingly.
Beauloye C., Vrolix M., Claeys M.J., van de Borne P., Vandendriessche E, & Van De Werf F. (2016). Pre-hospital management of acute coronary syndrome patients in Belgium and Luxembourg and other Western European countries. Acta cardiologica, 71(1).
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